[R] Re: Load prediction

[ripley@stats.ox.ac.uk]

On Sun, 23 Jun 2002, Johanus Dagius wrote:

> Dr. Ripley,

Who (s)he?

> I am not comparing R and Cubist on the same level. The
> reason I am interested in R is because it is, as you
> say, extensible and very flexible. (But much more
> difficult to master). I hope it allow me to build
> models (like Cubist),  but also visualize and analyze
> the results (which Cubist is not designed to do).
>
> I have already used libsvm and know about neural nets,
> but what is this "VR bundle"? Is it a CRAN package?

Yes, it a recommended package, part of a standard R installation.

>
> Thank you,
> Johanus Dagius
>
> --- ripley at stats.ox.ac.uk wrote:
> > On Sat, 22 Jun 2002, Johanus Dagius wrote:
> >
> > > Hello,
> > >
> > > I have received no reply to my previous query, so
> > I
> > > will try again.
> > >
> > > I have tried glm on this problem with the default
> > > parameters and it produced a model with mean
> > absolute
> > > error of approx 300 MWhrs. (The data is roughly
> > > normally distributed with a mean of 1700 MWhrs and
> > > SD=500). I know very little about R and so I am
> > not
> > > sure what parameter needs to be tweaked from here.
> > >
> > > Using Cubist (www.rulequest.com) I have created a
> > > predictive model whose mean error is around 100
> > MWhrs.
> > > Cubist builds a recursively partitioned tree using
> > > piecewise linear regression. Cubist also outputs a
> > > nice set of rules which explain the model in terms
> > of
> > > feature splits.
> > >
> > > I think R should give a comparable result. Does R
> > have
> > > a method of piecewise approximation like this? I
> > would
> > > like to compare R against Cubist. What method(s)in
> > R
> > > must I learn to do this?
> >
> > R is an extensible software system, not a set of
> > model-building
> > techniques.  You really didn't tell us anything like
> > enough (either time)
> > about your data.  (E.g. Cubist is designed for
> > thousands of records and
> > tens to hundreds of variables: you showed five and
> > around seven.)  But as
> > a general principle, this looks as if glm (as
> > distinct from lm) is not
> > needed, and the currently most promising prediction
> > techniques for
> > continuous quantities are thought to be neural
> > networks (in the VR bundle)
> > and SVMs (in package e1071). R also has several
> > packages for tree-building
> > (see the FAQ), and you could implement something
> > very like Cubist in R.
> > So `to compare R against Cubist' is not
> > well-defined, both for `R' and for
> > the criteria to be used.
> >
> > My advice would be to engage a statistical
> > consultant to guide you.
> >
> >
> > > At 12:13 PM 6/21/02 -0700, I wrote:
> > > > Hello,
> > > >
> > > >This is perhaps more of a regression question
> > than R,
> > > >but I am learning both, so would appreciate your
> > > >wisdom here.
> > > >
> > > >
> > > >I have some data which reflects power load for an
> > > >electrical generating system, with some temporal
> > > >features. The data fields look like this:
> > > >
> > > >
> > > >ID,MON,DAY,YR,HR,WDAY,DRYBULB,WETBULB,LOAD
> > > >4455   5  13   92   13    4      70      63 1617
> > > >4456   3   9   92   13    2      73      57 1397
> > > >4457  10   5   92    8    2      58      58 1501
> > > >4458  11  24   92   18    3      56      56 1885
> > > >4459   9  27   92    8    1      65      65 1402
> > > >
> > > >
> > > >What R methodology is likely to produce the most
> > > >accurate load forecast prediction for a given
> > date
> > > and
> > > >temperatures for problems like this?
> > > >
> > > >
> > > >Thank you,
> > > >Johanus Dagius
> > >
> > >
> > > __________________________________________________
> > >
> > >
> > >
> > >
> >
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> > >
> >
> > --
> > Brian D. Ripley,
> > ripley at stats.ox.ac.uk
> > Professor of Applied Statistics,
> > http://www.stats.ox.ac.uk/~ripley/
> > University of Oxford,             Tel:  +44 1865
> > 272861 (self)
> > 1 South Parks Road,                     +44 1865
> > 272860 (secr)
> > Oxford OX1 3TG, UK                Fax:  +44 1865
> > 272595
> >
>
>
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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